期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2012
卷号：2
期号：6
页码：766-773
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：In this paper, we introduce the new method of Extraction and Analysis of Non-linear Features (EANF) for face recognition based on extraction and analysis of nonlinear features i.e. Locality Preserving Analysis. In our proposed algorithm, EANF removes disadvantages such as the length of search space, different sizes and qualities of imagees due to various conditions of imaging time that has led to problems in the previous algorithms and removes the disadvantages of ELPDA methods (local neighborhood separator analysis) using the Scatter matrix in the form of a between-class scatter that this matrix introduces and displayes the nearest neighbors to K of the outer class by the samples. In addition, another advantage of EANF is high-speed in the face recognition through miniaturizing the size of feature matrix by NLPCA (Non-Linear Locality Preserving Analysis). Finally, the results of tests on FERET Dataset show the impact of the proposed method on the face recognition. DOI: http://dx.doi.org/10.11591/ijece.v2i6.1773
其他摘要：In this paper, we introduce the new method of Extraction and Analysis of Non-linear Features (EANF) for face recognition based on extraction and analysis of nonlinear features i.e. Locality Preserving Analysis. In our proposed algorithm, EANF removes disadvantages such as the length of search space, different sizes and qualities of imagees due to various conditions of imaging time that has led to problems in the previous algorithms and removes the disadvantages of ELPDA methods (local neighborhood separator analysis) using the Scatter matrix in the form of a between-class scatter that this matrix introduces and displayes the nearest neighbors to K of the outer class by the samples. In addition, another advantage of EANF is high-speed in the face recognition through miniaturizing the size of feature matrix by NLPCA (Non-Linear Locality Preserving Analysis). Finally, the results of tests on FERET Dataset show the impact of the proposed method on the face recognition. DOI: http://dx.doi.org/10.11591/ijece.v2i6.1773
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